349 citations as recorded by crossref.

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2. FOCA: a new quality-controlled database of floods and catchment descriptors in Italy P. Claps et al. 10.5194/essd-16-1503-2024
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16. Catchment response to climatic variability: implications for root zone storage and streamflow predictions N. Tempel et al. 10.5194/hess-28-4577-2024
17. A hydrologic signature approach to analysing wildfire impacts on overland flow L. Bolotin & H. McMillan 10.1002/hyp.15215
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19. From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling W. Tsai et al. 10.1038/s41467-021-26107-z
20. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale? W. Zhi et al. 10.1021/acs.est.0c06783
21. Design flood estimation using extreme Gradient Boosting-based on Bayesian optimization D. Jarajapu et al. 10.1016/j.jhydrol.2022.128341
22. airGRteaching: an open-source tool for teaching hydrological modeling with R O. Delaigue et al. 10.5194/hess-27-3293-2023
23. On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions S. Azimi et al. 10.5194/hess-27-4485-2023
24. A decomposition approach to evaluating the local performance of global streamflow reanalysis T. Zhao et al. 10.5194/hess-28-3597-2024
25. Improved Regionalization of the CN Method for Extreme Events at Ungauged Sites across the US T. Neelam et al. 10.1061/JHYEFF.HEENG-6180
26. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al. 10.5194/essd-15-4463-2023
27. Can We Use the Water Budget to Infer Upland Catchment Behavior? The Role of Data Set Error Estimation and Interbasin Groundwater Flow B. Gordon et al. 10.1029/2021WR030966
28. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset C. Alvarez-Garreton et al. 10.5194/hess-22-5817-2018
29. To What Extent Are Changes in Flood Magnitude Related to Changes in Precipitation Extremes? H. Do et al. 10.1029/2020GL088684
30. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi 10.5194/hess-26-2387-2022
31. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al. 10.1029/2019WR025975
32. Evaluating stochastic rainfall models for hydrological modelling T. Nguyen et al. 10.1016/j.jhydrol.2023.130381
33. rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al. 10.21105/joss.04237
34. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. 10.5194/essd-12-2459-2020
35. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
36. Introductory overview: Recommendations for approaching scientific visualization with large environmental datasets C. Kelleher & A. Braswell 10.1016/j.envsoft.2021.105113
37. A Hydrologic Functional Approach for Improving Large‐Sample Hydrology Performance in Poorly Gauged Regions J. Janssen & A. Ameli 10.1029/2021WR030263
38. Gauging ungauged catchments – Active learning for the timing of point discharge observations in combination with continuous water level measurements S. Pool & J. Seibert 10.1016/j.jhydrol.2021.126448
39. Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al. 10.1088/1748-9326/ad6fb7
40. Catchment characterization: Current descriptors, knowledge gaps and future opportunities L. Tarasova et al. 10.1016/j.earscirev.2024.104739
41. A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al. 10.5194/essd-14-3791-2022
42. The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records D. de Oliveira & J. Vrugt 10.1029/2022WR032263
43. Generalized Relationship Linking Water Balance and Vegetation Productivity across Site-to-Regional Scales G. Abeshu et al. 10.1061/JHYEFF.HEENG-6163
44. Time to Update the Split‐Sample Approach in Hydrological Model Calibration H. Shen et al. 10.1029/2021WR031523
45. Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers F. van Oorschot et al. 10.1088/1748-9326/ad8805
46. Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al. 10.1080/02626667.2022.2104646
47. Train, Inform, Borrow, or Combine? Approaches to Process‐Guided Deep Learning for Groundwater‐Influenced Stream Temperature Prediction J. Barclay et al. 10.1029/2023WR035327
48. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. 10.1029/2021WR030993
49. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al. 10.5194/hess-25-5355-2021
50. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132269
51. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model H. Yin et al. 10.1016/j.jhydrol.2021.126378
52. Spatial Delimitation of Small Headwater Catchments and Their Classification in Terms of Runoff Risks P. Kavka 10.3390/w13233458
53. Time Series Features for Supporting Hydrometeorological Explorations and Predictions in Ungauged Locations Using Large Datasets G. Papacharalampous & H. Tyralis 10.3390/w14101657
54. MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al. 10.1002/lol2.10325
55. Comprehensive assessment of baseflow responses to long-term meteorological droughts across the United States S. Lee & H. Ajami 10.1016/j.jhydrol.2023.130256
56. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al. 10.5194/hess-27-139-2023
57. Benchmarking high-resolution hydrologic model performance of long-term retrospective streamflow simulations in the contiguous United States E. Towler et al. 10.5194/hess-27-1809-2023
58. Future streamflow regime changes in the United States: assessment using functional classification M. Brunner et al. 10.5194/hess-24-3951-2020
59. Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) J. Mai et al. 10.1061/(ASCE)HE.1943-5584.0002097
60. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
61. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
62. The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis E. Le et al. 10.3389/fenvs.2023.1207508
63. Mapping (dis)agreement in hydrologic projections L. Melsen et al. 10.5194/hess-22-1775-2018
64. Uncertainties in measuring and estimating water‐budget components: Current state of the science S. Levin et al. 10.1002/wat2.1646
65. Dual-Stage Attention-Based LSTM Network for Multiple Time Steps Flood Forecasting F. Wang et al. 10.5194/piahs-386-141-2024
66. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
67. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
68. Beyond Counting Zeroes: Using Entropy-Based Hydrologic Signatures and Classification for Streamflow Intermittency Assessment I. Niadas & C. Makropoulos 10.1007/s11269-024-03881-1
69. Adaptive Reservoir Inflow Forecasting Using Variational Mode Decomposition and Long Short-Term Memory H. Hu et al. 10.1109/ACCESS.2021.3107502
70. HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al. 10.5194/hess-26-4773-2022
71. Streamflow-based evaluation of climate model sub-selection methods J. Kiesel et al. 10.1007/s10584-020-02854-8
72. Many Commonly Used Rainfall‐Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections K. Fowler et al. 10.1029/2019WR025286
73. Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study S. Pool et al. 10.1029/2021WR030437
74. Runoff for Russia (RFR v1.0): The Large-Sample Dataset of Simulated Runoff and Its Characteristics G. Ayzel 10.3390/data8020031
75. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. 10.3390/w16131904
76. Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US G. Konapala et al. 10.1088/1748-9326/aba927
77. Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments P. David et al. 10.1029/2021WR030619
78. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
79. Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff D. Althoff & G. Destouni 10.1016/j.oneear.2023.08.002
80. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
81. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al. 10.5194/essd-12-2075-2020
82. Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
83. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
84. Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs S. Bhanja et al. 10.1016/j.jhydrol.2023.129176
85. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
86. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez 10.1016/j.advwatres.2024.104796
87. Interactions between snow cover and evaporation lead to higher sensitivity of streamflow to temperature A. Meira Neto et al. 10.1038/s43247-020-00056-9
88. Modeling hydraulic heads with impulse response functions in different environmental settings of the Baltic countries M. Jemeļjanova et al. 10.1016/j.ejrh.2023.101416
89. Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al. 10.5194/hess-28-945-2024
90. Flood Frequency Modeling and Prediction of Beki and Pagladia Rivers Using Deep Learning Approach G. Devi et al. 10.1007/s11063-022-10773-1
91. Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032 T. van Hateren et al. 10.1080/02626667.2023.2170754
92. Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction T. Xie et al. 10.3390/w16010069
93. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. 10.5194/essd-16-1559-2024
94. On the challenges of global entity-aware deep learning models for groundwater level prediction B. Heudorfer et al. 10.5194/hess-28-525-2024
95. Higher-order internal modes of variability imprinted in year-to-year California streamflow changes S. Duan et al. 10.1038/s43247-024-01594-2
96. Comparison of eight filter-based feature selection methods for monthly streamflow forecasting – Three case studies on CAMELS data sets K. Ren et al. 10.1016/j.jhydrol.2020.124897
97. Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al. 10.3389/frwa.2023.1126310
98. A benchmark dataset for machine learning in ecotoxicology C. Schür et al. 10.1038/s41597-023-02612-2
99. Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach G. Konapala et al. 10.1029/2020WR027340
100. Using R in hydrology: a review of recent developments and future directions L. Slater et al. 10.5194/hess-23-2939-2019
101. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar 10.5194/hess-26-2019-2022
102. A Novel Approach for High-Performance Estimation of SPI Data in Drought Prediction L. Latifoğlu & M. Özger 10.3390/su151914046
103. Climate and rivers G. McGregor 10.1002/rra.3508
104. Streamflow regime of a lake‐stream system based on long‐term data from a high‐density hydrometric network D. Hudson et al. 10.1002/hyp.14396
105. Runoff Prediction Based on Deep Residual Shrinkage Long Short-term Memory Network Z. Guan et al. 10.1088/1742-6596/2400/1/012016
106. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
107. Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems E. Uncuoglu et al. 10.1016/j.asoc.2022.109623
108. Catchment processes can amplify the effect of increasing rainfall variability M. Müller et al. 10.1088/1748-9326/ac153e
109. Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
110. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
111. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
112. Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al. 10.5194/essd-15-2781-2023
113. Dissolving the mystery of subsurface controls on snowmelt–discharge dynamics in karst mountain watersheds using hydrologic timeseries D. Thurber et al. 10.1002/hyp.15170
114. Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics J. Frame et al. 10.1111/1752-1688.12964
115. Solute export patterns across the contiguous USA D. Kincaid et al. 10.1002/hyp.15197
116. An Efficient Estimation of Time‐Varying Parameters of Dynamic Models by Combining Offline Batch Optimization and Online Data Assimilation Y. Sawada 10.1029/2021MS002882
117. Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al. 10.3178/hrl.16.73
118. Theoretical and empirical evidence against the Budyko catchment trajectory conjecture N. Reaver et al. 10.5194/hess-26-1507-2022
119. Hydrologic Sensitivities and ENSO Variability Across Hydrological Regimes in Central Chile (28°–41°S) D. Hernandez et al. 10.1029/2021WR031860
120. Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning S. Jiang et al. 10.1029/2020GL088229
121. A unified framework of water balance models for monthly, annual, and mean annual timescales X. Zhang et al. 10.1016/j.jhydrol.2020.125186
122. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. 10.5194/hess-26-1673-2022
123. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
124. A Novel Strategy for Automatic Selection of Cross‐Basin Data to Improve Local Machine Learning‐Based Runoff Models C. Nai et al. 10.1029/2023WR035051
125. Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization S. Ragettli et al. 10.1016/j.jhydrol.2017.10.031
126. A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves C. Leong & Y. Yokoo 10.1002/hyp.14569
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128. A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes S. Chen & X. Ruan 10.1016/j.jhydrol.2023.129118
129. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
130. A new criteria for determining the best decomposition level and filter for wavelet-based data-driven forecasting frameworks- validating using three case studies on the CAMELS dataset M. Mazarei Behbahani & A. Mazarei 10.1007/s00477-023-02531-z
131. Direct vegetation response to recent CO2 rise shows limited effect on global streamflow H. Wei et al. 10.1038/s41467-024-53879-x
132. Meta-LSTM in hydrology: Advancing runoff predictions through model-agnostic meta-learning K. Cai et al. 10.1016/j.jhydrol.2024.131521
133. Vegetation patterns governing the competitive relationship between runoff and evapotranspiration using a novel water balance model at a semi-arid watershed Q. Fang et al. 10.1016/j.envres.2022.113976
134. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
135. Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow H. Tyralis & G. Papacharalampous 10.5194/adgeo-45-147-2018
136. National-scale geodatabase of catchment characteristics in the Philippines for river management applications R. Boothroyd et al. 10.1371/journal.pone.0281933
137. Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions K. Ma et al. 10.1029/2020WR028600
138. Diagnostic Evaluation of Large‐Domain Hydrologic Models Calibrated Across the Contiguous United States O. Rakovec et al. 10.1029/2019JD030767
139. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
140. Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique S. Huang et al. 10.1007/s00477-021-02057-2
141. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K. Xie et al. 10.1016/j.jhydrol.2021.127043
142. Quantifying the Geomorphic Effect of Floods Using Satellite Observations of River Mobility A. Leenman et al. 10.1029/2023GL103875
143. Seasonality and Drivers of Low Flows Across Europe and the United States M. Floriancic et al. 10.1029/2019WR026928
144. Spatially Distributed Overstory and Understory Leaf Area Index Estimated from Forest Inventory Data S. Goeking & D. Tarboton 10.3390/w14152414
145. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
146. What control the spatial patterns and predictions of runoff response over the contiguous USA? S. Jiang et al. 10.1007/s11442-024-2249-4
147. Technical note: Do different projections matter for the Budyko framework? R. Nijzink & S. Schymanski 10.5194/hess-26-4575-2022
148. Variational Bayesian dropout with a Gaussian prior for recurrent neural networks application in rainfall–runoff modeling S. Sadeghi Tabas & S. Samadi 10.1088/1748-9326/ac7247
149. The importance of source data in river network connectivity modeling: A review C. Brinkerhoff 10.1002/lno.12706
150. What is the hydrologically effective area of a catchment? Y. Liu et al. 10.1088/1748-9326/aba7e5
151. The role of previously glaciated landscapes in spatiotemporal variability of streamflow in snow-dominated watersheds: British Columbia, Canada X. Chen et al. 10.1016/j.ejrh.2023.101478
152. Horton Index: Conceptual Framework for Exploring Multi‐Scale Links Between Catchment Water Balance and Vegetation Dynamics G. Abeshu & H. Li 10.1029/2020WR029343
153. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al. 10.5194/hess-28-3665-2024
154. Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential T. Fadziso 10.18034/ajase.v8i1.10
155. Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems U. Ehret & P. Dey 10.5194/hess-27-2591-2023
156. Variable Streamflow Response to Forest Disturbance in the Western US: A Large‐Sample Hydrology Approach S. Goeking & D. Tarboton 10.1029/2021WR031575
157. AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods A. Abbas et al. 10.5194/gmd-15-3021-2022
158. Machine‐Learning Reveals Equifinality in Drivers of Stream DOC Concentration at Continental Scales K. Underwood et al. 10.1029/2021WR030551
159. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
160. Climate Controls on River Chemistry L. Li et al. 10.1029/2021EF002603
161. Hydrological Drought Simulations: How Climate and Model Structure Control Parameter Sensitivity L. Melsen & B. Guse 10.1029/2019WR025230
162. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
163. The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset G. Ayzel & M. Heistermann 10.1016/j.cageo.2021.104708
164. Modeling streamflow variability at the regional scale: (1) perceptual model development through signature analysis F. Fenicia & J. McDonnell 10.1016/j.jhydrol.2021.127287
165. Crossing the rural–urban boundary in hydrological modelling: How do conceptual rainfall–runoff models handle the specificities of urbanized catchments? M. Saadi et al. 10.1002/hyp.13808
166. A step toward global-scale applicability and transferability of flow duration curve studies: A flow duration curve review (2000–2020) C. Leong & Y. Yokoo 10.1016/j.jhydrol.2021.126984
167. How Do Climate and Catchment Attributes Influence Flood Generating Processes? A Large‐Sample Study for 671 Catchments Across the Contiguous USA L. Stein et al. 10.1029/2020WR028300
168. Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA T. Barnhart et al. 10.1002/rra.4028
169. On the use of distribution-adaptive likelihood functions: Generalized and universal likelihood functions, scoring rules and multi-criteria ranking J. Vrugt et al. 10.1016/j.jhydrol.2022.128542
170. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
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173. A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes P. Dimitriadis et al. 10.3390/hydrology8020059
174. Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors X. Li et al. 10.1029/2021WR031794
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176. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets F. Kratzert et al. 10.5194/hess-23-5089-2019
177. Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management L. Slater et al. 10.5194/hess-25-3897-2021
178. Hydrological post-processing for predicting extreme quantiles H. Tyralis & G. Papacharalampous 10.1016/j.jhydrol.2023.129082
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245. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. 10.3390/w13081048
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256. Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges E. Ragno et al. 10.5194/hess-26-1695-2022
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261. Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates S. Saxe et al. 10.5194/hess-25-1529-2021
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265. Exploring the provenance of information across Canadian hydrometric stations: implications for discharge estimation and uncertainty quantification S. Gharari et al. 10.5194/hess-28-4383-2024
266. Improving the Representation of Subsurface Water Movement in Land Models S. Gharari et al. 10.1175/JHM-D-19-0108.1
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268. Comparison of flood simulation capabilities of a hydrologic model and a machine learning model L. Liu et al. 10.1002/joc.7738
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273. Direct and Indirect Drivers of Energy and Nutrient Availability in Freshwater Ecosystems Across Spatial Scales H. Fazekas et al. 10.1029/2022GB007580
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276. Soil CO2 Controls Short‐Term Variation but Climate Regulates Long‐Term Mean of Riverine Inorganic Carbon B. Stewart et al. 10.1029/2022GB007351
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280. Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America J. Janssen et al. 10.3389/feart.2021.730631
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313. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
314. Earthquake Nowcasting with Deep Learning G. Fox et al. 10.3390/geohazards3020011
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317. Is There a Baseflow Budyko Curve? S. Gnann et al. 10.1029/2018WR024464
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323. Using Physics-Encoded GeoAI to Improve the Physical Realism of Deep Learning′s Rainfall-Runoff Responses under Climate Change H. Li et al. 10.1016/j.jag.2024.104101
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327. Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al. 10.5194/essd-16-2073-2024
328. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. 10.5194/hess-28-4187-2024
329. Inference of Parameters for a Global Hydrological Model: Identifiability and Predictive Uncertainties of Climate‐Based Parameters T. Yoshida et al. 10.1029/2021WR030660
330. A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds R. Arsenault et al. 10.1038/s41597-020-00583-2
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334. Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling K. Li et al. 10.5194/hess-25-4947-2021
335. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen 10.5194/essd-16-2741-2024
336. A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes J. Quilty & J. Adamowski 10.1016/j.envsoft.2020.104718
337. Findings from a National Survey of Canadian perspectives on predicting river channel migration and river bank erosion C. Kupferschmidt & A. Binns 10.1002/rra.4336
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339. Data‐Driven Worldwide Quantification of Large‐Scale Hydroclimatic Covariation Patterns and Comparison With Reanalysis and Earth System Modeling N. Ghajarnia et al. 10.1029/2020WR029377
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342. Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben 10.1002/hyp.15288
343. When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al. 10.5194/hess-28-3051-2024
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346. To What Extent Are Changes in Flood Magnitude Related to Changes in Precipitation Extremes? H. Do et al. 10.1029/2020GL088684
347. BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al. 10.1038/s41597-024-03594-5
348. Hydrological Interpretation of a Statistical Measure of Basin Complexity S. Pande & M. Moayeri 10.1029/2018WR022675
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